Estimating the risk reduction of isolation on COVID-19 non-household transmission and severe/critical illness in non-immune individuals: September to November 2021
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Abstract
In the fall 2021, immunity mandates/passports for COVID-19 started to be discussed and implemented globally. In addition to increasing vaccination levels, these interventions isolate non-immune individuals from various settings to reduce non-household transmission and severe/critical illness. This is based on the hypothesis that the non-immune are at high absolute risk of these outcomes. However, these absolute risks were not quantified in the literature such that the absolute risk reductions of isolation on these outcomes remain unknown. This study estimated these absolute risks from September to November 2021 prior to the emergence of Omicron (B.1.1.529) using known data on the risk of infection, transmission in non-household settings, and age-stratified severe/critical illness in non-immune individuals for the Delta (B.1.617.2) variant, focusing on the European Union, United Kingdom, United States, Canada, Australia, and Israel. This allowed us to quantify the absolute risk reductions of isolation on (1) non-household transmission from the non-immune and (2) severe/critical illness amongst the non-immune in these regions during this period. We observed that on any given day the absolute risk reductions of isolation were typically small for transmission in most types of non-household settings and severe/critical illness in most age-groups, especially those aged <40. During a wave or sustained higher infection risks, the risk reductions were modest only for transmission in intimate social gatherings and severe/critical illness in adults aged ≥50-60. The limitations of this study and the implications for the expected benefits of isolating non-immune individuals on reducing these outcomes are discussed.
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SciScore for 10.1101/2022.02.05.22270453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A limitation of this analysis is that we had to extrapolate from wild-type data to estimate the ARs for Delta infections given that there was insufficient direct data. There is also likely a degree of underestimation in the DRDC database of the IRs. It is difficult to accurately model underreporting rates and how they change over time because one is trying to model something where there are no data. This is shown by the extreme …
SciScore for 10.1101/2022.02.05.22270453: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A limitation of this analysis is that we had to extrapolate from wild-type data to estimate the ARs for Delta infections given that there was insufficient direct data. There is also likely a degree of underestimation in the DRDC database of the IRs. It is difficult to accurately model underreporting rates and how they change over time because one is trying to model something where there are no data. This is shown by the extreme variation in underreporting estimates [4, 23]. Local context/knowledge is required to estimate underreporting rates in a region over time, which is not available on a global scale. Estimating the NNIs required using publicly available data in published and preprint reports, such that several included studies have not yet been fully peer-reviewed. This was unavoidable due to the newly emerging evidence base on this topic and the multiple month lag from the peer-review process. It is reasonable to ask why we did not use a risk metric to estimate the IR which uses a longer period of time (e.g., incidence proportion, period prevalence) since longer time windows would increase the IRs and thus lower the NNIs. Point-prevalence is the more appropriate metric for IR than incidence proportion and period prevalence for four reasons. First, the infection risk depends not just on new cases, but existing ones too. Second, incidence proportion and period prevalence depend on the time at risk. In general, shorter time windows will lower these metrics than longer time...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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